Unbiased learning to rank (ULTR) studies the problem of mitigating various biases from implicit user feedback data such as clicks, and has been receiving considerable attention recently. A popular ULTR approach for real-world applications uses a two-tower architecture, where click modeling is factorized into a relevance tower with regular input features, and a bias tower with bias-relevant inputs such as the position of a document. A successful factorization will allow the relevance tower to be exempt from biases. In this work, we identify a critical issue that existing ULTR methods ignored - the bias tower can be confounded with the relevance tower via the underlying true relevance. In particular, the positions were determined by the logging policy, i.e., the previous production model, which would possess relevance information. We give both theoretical analysis and empirical results to show the negative effects on relevance tower due to such a correlation. We then propose three methods to mitigate the negative confounding effects by better disentangling relevance and bias. Empirical results on both controlled public datasets and a large-scale industry dataset show the effectiveness of the proposed approaches.

Automatic parsing of human anatomies at instance-level from 3D computed tomography (CT) scans is a prerequisite step for many clinical applications. The presence of pathologies, broken structures or limited field-of-view (FOV) all can make anatomy parsing algorithms vulnerable. In this work, we explore how to exploit and conduct the prosperous detection-then-segmentation paradigm in 3D medical data, and propose a steerable, robust, and efficient computing framework for detection, identification, and segmentation of anatomies in CT scans. Considering complicated shapes, sizes and orientations of anatomies, without lose of generality, we present the nine degrees-of-freedom (9-DoF) pose estimation solution in full 3D space using a novel single-stage, non-hierarchical forward representation. Our whole framework is executed in a steerable manner where any anatomy of interest can be directly retrieved to further boost the inference efficiency. We have validated the proposed method on three medical imaging parsing tasks of ribs, spine, and abdominal organs. For rib parsing, CT scans have been annotated at the rib instance-level for quantitative evaluation, similarly for spine vertebrae and abdominal organs. Extensive experiments on 9-DoF box detection and rib instance segmentation demonstrate the effectiveness of our framework (with the identification rate of 97.0% and the segmentation Dice score of 90.9%) in high efficiency, compared favorably against several strong baselines (e.g., CenterNet, FCOS, and nnU-Net). For spine identification and segmentation, our method achieves a new state-of-the-art result on the public CTSpine1K dataset. Last, we report highly competitive results in multi-organ segmentation at FLARE22 competition. Our annotations, code and models will be made publicly available at: https://github.com/alibaba-damo-academy/Med_Query.

反转合是药物发现的主要任务。通过许多现有方法，它被称为生成图的问题。具体而言，这些方法首先识别反应中心，并相应地打破靶分子以生成合成子。反应物是通过顺序添加到合成图或直接添加正确的离开组来生成反应物。但是，两种策略都遭受了添加原子以来会导致长期的预测顺序，从而增加了产生难度，同时添加离开组只能考虑训练集中的序列，从而导致概括不佳。在本文中，我们提出了一个新颖的端到端图生成模型，用于逆转录合成预测，该模型顺序识别反应中心，生成合成子，并将基序添加到合成子中以生成反应物。由于化学有意义的基序比原子大，比离开组还小，因此与添加原子相比，与添加离开组相比，我们的方法的预测复杂性较低。基准数据集上的实验表明，所提出的模型显着胜过先前的最新算法。

由于复杂的腹部内形状和腹部器官之间的复杂形状和外观变化，从不同模态的CT成像中进行的准确且健壮的腹部多器官分割是一项具有挑战性的任务。在本文中，我们提出了一个具有分层空间特征调制的概率多器官分割网络，以捕获灵活的器官语义变体，并将学习的变体注入不同的特征图尺度，以进行指导分割。更具体地说，我们通过条件变异自动编码器设计一个输入分解模块，以在低维潜在空间和模型富有器官语义变化上学习器官特异性分布，该分布在输入图像上进行条件。 -NET解码器通过空间特征转换从层次上进行分层，该特征转换能够将变化转换为空间特征映射调制并指导细尺度分割的条件仿射转换参数。提出的方法对公开可用的腹部可用数据集进行了培训，并在其他两个开放数据集上进行了评估，即100个挑战/病理测试，从腹部腹部1K完全监督的腹部器官细分基准和90例TCIA+＆BTCV数据集中进行了90例病例。使用这些数据集用于四个腹部器官，肾脏，脾脏和胰腺，肾脏分数提高了7.3％，胰腺的骰子得分提高了7.7％，而胰腺的骰子得分提高了7.3％，而胰腺的较高速度比强度快7倍，较高的7倍基线分割方法（NNUNET和COTR）。

病理系统地诱导形态学变化，从而提供了主要但不足以量化的可观察到诊断来源。该研究基于计算机断层扫描（CT）体积的形态特征（3D形态学）开发了病理状态的预测模型。开发了一个完整的工作流程，以进行网状提取和简化器官表面的工作流程，并与平均曲率和网状能的分布自动提取形态特征自动提取。然后对XGBoost监督分类器进行了训练和测试，以预测病理状态。该框架应用于肺结节恶性肿瘤的预测。在具有恶性肿瘤的NLST数据库的子集中，仅使用3D形态学证实了活检，将肺结节的分类模型分类为恶性与良性AUC的良性0.964。 （1）临床相关特征的其他三组经典特征经过训练和测试，AUC为0.58，（2）111辐射因子学的AUC为0.976，（3）含有结节大小，衰减和衰减和衰减的放射科医生地面真相（GT） Spiculation定性注释的AUC为0.979。我们还测试了Brock模型并获得0.826的AUC。将3D形态学和放射素学特征结合在一起，可以实现最新的结果，而AUC为0.978，其中3D形态学具有一些最高的预测能力。作为对公共独立队列的验证，将模型应用于LIDC数据集，3D形态学的AUC达到0.906，而3D型物体+放射线学则获得了0.958的AUC，在挑战中排名第二。它将曲率分布确定为预测肺结核恶性肿瘤的有效特征，并可以直接应用于任意计算机辅助诊断任务。

多字符分类（MCC）是一个基本机器学习问题，其旨在将每个实例分类为预定义的类集中的一个。鉴于实例，分类模型计算每个类的分数，然后所有类别都用于对类进行排序。分类模型的性能通常通过TOP-K精度/误差（例如，k = 1或5）来测量。在本文中，我们不会旨在提出新的神经表征学习模型，因为最近的作品，但要表明通过排名镜头可以轻松提高MCC性能。特别是，通过将MCC视为对实例的等级等级，我们首先争辩说排名指标，例如归一化的折扣累积增益（NDCG），可以比现有的Top-K度量更具信息化。我们进一步证明主导的神经MCC架构可以用特定的设计选择制定为神经排名框架。基于这种概括，我们表明，利用丰富的信息检索文献利用技术将技术效果简单，直观地将MCC性能从盒子中提高。具有不同数据集和骨干型号的文本和图像分类任务的广泛经验结果（例如，用于文本和图像分类的BERT和RESET）显示了我们提出的框架的价值。

节点之间有序序列的动态图在现实世界的工业应用中普遍存在电子商务和社交平台中。然而，由于数据的时间和结构依赖性和不规则性，因此，对动态图表的表示学习已经提出了很大的计算挑战，防止这些模型部署到现实世界的应用程序。为了解决这一挑战，我们提出了一种有效的算法，有效的动态图学习（边缘），它通过训练丢失选择性地表达某些时间依赖性，以改善计算中的并行性。我们展示了边缘可以扩展到数百万节点的动态图形，数亿个时间事件，实现新的最先进的（SOTA）性能。

风险的准确器官（OAR）分割对于减少治疗后并发症的放射治疗至关重要。达人指南推荐头部和颈部（H＆N）区域的一套超过40桨的桨，然而，由于这项任务的可预测的禁止劳动力成本，大多数机构通过划定较小的桨子和忽视的少数，选择了大量简化的协议与其他桨相关的剂量分布。在这项工作中，我们提出了一种使用深度学习的新颖，自动化和高效的分层OAR分段（SOARS）系统，精确地描绘了一套全面的42 H＆N OAR。 SOARS将42桨分层进入锚，中级和小型和硬质子类别，通过神经结构搜索（NAS）原则，专门为每个类别提供神经网络架构。我们在内在机构中使用176名培训患者建立了SOAR模型，并在六个不同的机构中独立评估了1327名外部患者。对于每个机构评估，它始终如一地表现出其他最先进的方法至少3-5％的骰子得分（在其他度量的相对误差减少36％）。更重要的是，广泛的多用户研究明显证明，98％的SOARE预测只需要非常轻微或没有直接临床验收的修订（节省90％的辐射脑神经工作负载），并且它们的分割和剂量准确度在于或小于帧 - 用户的变化。这些调查结果证实了H＆N癌症放射疗法工作流OAR描绘过程的强烈临床适用性，提高了效率，全面性和质量。

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.